Cardiovascular diseases currently present a key health concern, contributing to an increase in death rates worldwide. In this phase of increasing mortality rates, healthcare represents a major field of research, and the knowledge acquired from this analysis of health information will assist in the early identification of disease. The retrieval of medical information is becoming increasingly important to make an early diagnosis and provide timely treatment. Medical image segmentation and classification is an emerging field of research in medical image processing. In this research, the data collected from an Internet of Things (IoT)-based device, the health records of patients, and echocardiogram images are considered. The images are pre-processed and segmented, and then further processed using deep learning techniques for classification as well as forecasting the risk of heart disease. Segmentation is attained via fuzzy C-means clustering (FCM) and classification using a pretrained recurrent neural network (PRCNN). Based on the findings, the proposed approach achieves 99.5% accuracy, which is higher than the current state-of-the-art techniques.

A Mathematical Queuing Model Analysis Using Secure Data Authentication Framework for Modern Healthcare Applications: Healthcare application is one of the most promising developments to provide on-time demand services to the end users, vehicles, and other Road Side Units (RSUs) in the urban environment. In recent years, several application interfaces have been developed to connect, communicate, and share the required services from one source to another. However, the urban environment holds a complex entity of both homogenous and heterogeneous devices to which the communication/sensing range between the devices leads to connectivity breakage, lack of needed service in time, and other environmental constraints. Also, security plays a vital role in allowing everyone in the urban area to access/request services according to their needs. Again, this leads to a massive breakthrough in providing reliable service to authentic users or a catastrophic failure of service denial involving unauthorized user access. This paper proposes a novel topological architecture, Secure Authentication Relay-based Urban Network (S-ARUN), designed for healthcare and other smart city applications for registered transportation stakeholders. The registered stakeholders hold a built-in data security framework with three subsystems connected to the S-ARUN topology: (1) authentication subsystem: the stakeholder must identify themselves to the source responder as part of the authentication subsystem before transmitting the actual data service request; (2) connectivity subsystem: to periodically check the connection state of stakeholders as they travel along with the road pattern; and (3) service subsystem: each source responder will keep a separate queue for collecting data service requests, processing them quickly, and sending the results to the appropriate stakeholder. The Kerberos authentication method is used in working with S-ARUNs model to connect the stakeholders securely and legitimately. The performance of the proposed S-ARUN is assessed, and the performance metric toward key generation and other data security-related metrics is tested with existing schemes.